EdgeFaaS: A Function-based Framework for Edge Computing

The rapid growth of data generated from Internet of Things (IoTs) such as smart phones and smart home devices presents new challenges to cloud computing in transferring, storing, and processing the data. With increasingly more powerful edge devices, edge computing, on the other hand, has the potential to better responsiveness, privacy, and cost efficiency. However, resources across the cloud and edge are highly distributed and highly diverse. To address these challenges, this paper proposes EdgeFaaS, a Function-as-a-Service (FaaS) based computing framework that supports the flexible, convenient, and optimized use of distributed and heterogeneous resources across IoT, edge, and cloud systems. EdgeFaaS allows cluster resources and individual devices to be managed under the same framework and provide computational and storage resources for functions. It provides virtual function and virtual storage interfaces for consistent function management and storage management across heterogeneous compute and storage resources. It automatically optimizes the scheduling of functions and placement of data according to their performance and privacy requirements. EdgeFaaS is evaluated based on two edge workflows: video analytics workflow and federated learning workflow, both of which are representative edge applications and involve large amounts of input data generated from edge devices

Efficient HDR Reconstruction from Real-World Raw Images

High dynamic range (HDR) imaging is still a significant yet challenging problem due to the limited dynamic range of generic image sensors. Most existing learning-based HDR reconstruction methods take a set of bracketed-exposure sRGB images to extend the dynamic range, and thus are computational- and memory-inefficient by requiring the Image Signal Processor (ISP) to produce multiple sRGB images from the raw ones. In this paper, we propose to broaden the dynamic range from the raw inputs and perform only one ISP processing for the reconstructed HDR raw image. Our key insights are threefold: (1) we design a new computational raw HDR data formation pipeline and construct the first real-world raw HDR dataset, RealRaw-HDR; (2) we develop a lightweight-efficient HDR model, RepUNet, using the structural re-parameterization technique; (3) we propose a plug-and-play motion alignment loss to mitigate motion misalignment between short- and long-exposure images. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in both visual quality and quantitative metrics

When Edge Meets FaaS: Opportunities and Challenges

The proliferation of edge devices and the rapid growth of IoT data have called forth the edge computing paradigm. Function-as-a-service (FaaS) is a promising computing paradigm to realize edge computing. This paper explores the feasibility and advantages of FaaS-based edge computing. It also studies the research challenges that should be addressed in the design of such systems, which are 1) the quick decomposing and recomposing of applications, 2) the trade-off between performance and isolation of sandbox mechanisms, and 3) distributed scheduling. The challenges are illustrated by evaluating existing FaaS-based edge platforms, AWS IoT Greengrass, and OpenFaaS

Ferroelectricity controlled chiral spin textures and anomalous valley Hall effect in the Janus magnet-based multiferroic heterostructure

Realizing effective manipulation and explicit identification of topological spin textures are two crucial ingredients to make them as information carrier in spintronic devices with high storage density, high data handling speed and low energy consumption. Electric-field manipulation of magnetism has been achieved as a dissipationless method compared with traditional regulations. However, the magnetization is normally insensitive to the electric field since it does not break time-reversal symmetry directly, and distribution of topological magnetic quasiparticles is difficult to maintain due to the drift arising from external fluctuation, which could result in ambiguous recognition between quasiparticles and uniform magnetic background. Here, we demonstrate that electric polarization-driven skyrmionic and uniform ferromagnetic states can be easily and explicitly distinguished by transverse voltage arising from anomalous valley Hall effect in the Janus magnet-based multiferroic heterostructure LaClBr/In2Se3. Our work provides an alternative approach for data encoding, in which data are encoded by combing topological spin textures with detectable electronic transport.Comment: published in 2D materials, 9, 045030 (2022

EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes

Visual Emotion Analysis (VEA) aims at predicting people's emotional responses to visual stimuli. This is a promising, yet challenging, task in affective computing, which has drawn increasing attention in recent years. Most of the existing work in this area focuses on feature design, while little attention has been paid to dataset construction. In this work, we introduce EmoSet, the first large-scale visual emotion dataset annotated with rich attributes, which is superior to existing datasets in four aspects: scale, annotation richness, diversity, and data balance. EmoSet comprises 3.3 million images in total, with 118,102 of these images carefully labeled by human annotators, making it five times larger than the largest existing dataset. EmoSet includes images from social networks, as well as artistic images, and it is well balanced between different emotion categories. Motivated by psychological studies, in addition to emotion category, each image is also annotated with a set of describable emotion attributes: brightness, colorfulness, scene type, object class, facial expression, and human action, which can help understand visual emotions in a precise and interpretable way. The relevance of these emotion attributes is validated by analyzing the correlations between them and visual emotion, as well as by designing an attribute module to help visual emotion recognition. We believe EmoSet will bring some key insights and encourage further research in visual emotion analysis and understanding. Project page: https://vcc.tech/EmoSet.Comment: Accepted to ICCV2023, similar to the final versio

Dzyaloshinskii-Moriya interaction and magnetic skyrmions induced by curvature

Realizing sizeable Dzyaloshinskii-Moriya interaction (DMI) in intrinsic two-dimensional (2D) magnets without any manipulation will greatly enrich potential application of spintronics devices. The simplest and most desirable situation should be 2D magnets with intrinsic DMI and intrinsic chiral spin textures. Here, we propose to realize DMI by designing periodic ripple structures with different curvatures in low-dimensional magnets and demonstrate the concept in both one-dimensional (1D) CrBr2 and two-dimensional (2D) MnSe2 magnets by using first-principles calculations. We find that DMIs in curved CrBr2 and MnSe2 can be efficiently controlled by varying the size of curvature c, where c is defined as the ratio between the height h and the length l of curved structure. Moreover, we unveil that the dependence of first-principles calculated DMI on size of curvature c can be well described by the three-site Fert-L\'evy model. At last, we uncover that field-free magnetic skyrmions can be realized in curved MnSe2 by using atomistic spin model simulations based on first-principles calculated magnetic parameters. The work will open a new avenue for inducing DMI and chiral spin textures in simple 2D magnets via curvature.Comment: Published on Physical Review B 106, 05442

Symmetry-Preserving Program Representations for Learning Code Semantics

Large Language Models (LLMs) have shown promise in automated program reasoning, a crucial aspect of many security tasks. However, existing LLM architectures for code are often borrowed from other domains like natural language processing, raising concerns about their generalization and robustness to unseen code. A key generalization challenge is to incorporate the knowledge of code semantics, including control and data flow, into the LLM architectures. Drawing inspiration from examples of convolution layers exploiting translation symmetry, we explore how code symmetries can enhance LLM architectures for program analysis and modeling. We present a rigorous group-theoretic framework that formally defines code symmetries as semantics-preserving transformations and provides techniques for precisely reasoning about symmetry preservation within LLM architectures. Using this framework, we introduce a novel variant of self-attention that preserves program symmetries, demonstrating its effectiveness in generalization and robustness through detailed experimental evaluations across different binary and source code analysis tasks. Overall, our code symmetry framework offers rigorous and powerful reasoning techniques that can guide the future development of specialized LLMs for code and advance LLM-guided program reasoning tasks

Double band inversion in the topological phase transition of Ge1-xSnx alloys

We use first-principles simulation and virtual crystal approximation to reveal the unique double band inversion and topological phase transition in Ge1-xSnx alloys. Wavefunction parity, spatial charge distribution and surface state spectrum analyses suggest that the band inversion in Ge1-xSnx is relayed by its first valence band. As the system evolves from Ge to {\alpha}-Sn, its conduction band moves down, and inverts with the first and the second valence bands consecutively. The first band inversion makes the system nontrivial, while the second one does not change the topological invariant of the system. Both the band inversions yield surface modes spanning the individual inverted gaps, but only the surface mode in the upper gap associates with the nontrivial nature of tensile-strained {\alpha}-Sn.Comment: 5 pages, 6 figure